Building Tyvur: An End-to-End AI Interview Platform (Engineering Case Study)

Introduction

A few months ago, I built Tyvur, an AI-powered platform for practicing realistic technical interviews. Of everything I have shipped, this was one of the most technically challenging products I have built end to end.

The core idea was straightforward to describe but hard to implement: create an AI interviewer that could understand a candidate's resume and the role they are targeting, conduct a personalized interview, ask relevant questions, evaluate answers, and generate detailed feedback.

That required far more than connecting a UI to an LLM API. I built and deployed the full product myself: the AI layer, real-time voice flow, backend, frontend, auth, subscriptions, and payments.

This post covers what Tyvur does, what I built, and what it demonstrates for hiring teams. For the honest product retrospective (validation, retention, lessons learned), read Building Tyvur: Lessons From My First Real Product Attempt.

Project page: /projects/tyvur-ai

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Watch the Demo

I recorded a complete demo showing how the product works end to end:

• Uploading a resume and selecting a target role
• Starting a voice-based mock interview
• AI asking personalized questions with contextual follow-ups
• Real-time answer capture and evaluation
• Session feedback with specific improvement suggestions

If you are a recruiter or founder evaluating engineering depth, start here.

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The Problem I Was Building For

Most interview prep tools fall into two buckets:

1. Static question banks that do not adapt to your background
2. Text chatbots that do not simulate the pressure of speaking out loud

Neither matches what a real interview feels like. You can know the material and still freeze when someone asks you to explain it out loud, under time pressure, with a stranger evaluating every word.

Tyvur was built around a different goal: an AI interviewer that understands your resume, understands the role you are targeting, conducts a conversation that feels like a real interview, and tells you specifically where your answers were weak.

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What Tyvur Does

From a user's perspective, the product delivers:

• Personalized mock interviews based on resume and target role
• Voice-based practice that simulates real interview pressure
• Contextual follow-up questions that probe weak or vague answers
• Answer-level feedback with specific improvement suggestions
• Role-based interview modes aligned to what the candidate is preparing for

The experience is designed to feel like talking to an interviewer, not filling out a form.

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What Building Tyvur Actually Involved

It is worth being explicit about scope, because "AI interview app" undersells the engineering surface area.

Building Tyvur pushed me across the full stack:

• AI orchestration for multi-step, stateful interview flows
• State management so sessions survive disconnects and restarts
• Real-time communication for turn-based voice interviews
• Voice processing with speech-to-text in a conversational loop
• Backend and data layer for users, sessions, resumes, and history
• SaaS infrastructure including auth, subscriptions, and payment webhooks
• Production deployment as a live product handling real users

None of these layers is optional. Remove any one of them and you have a demo, not a product.

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Tech Stack

Python, FastAPI, LangGraph, LangChain, OpenAI, PostgreSQL, SQLAlchemy, WebSockets, Next.js, React, TypeScript, Docker, Nginx, and Razorpay.

This is not a notebook prototype. It is a live product at tyvur.in with auth, billing, and real user sessions.

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Challenges I Worked Through

These are the kinds of problems that only show up when you ship AI in production, not in a prototype:

• Personalized interviews from unstructured resumes in PDF and DOCX formats with inconsistent layouts
• Voice interviews that feel conversational, not like a rigid Q&A script
• Long-running sessions that need to stay coherent across many turns
• Reliability when external AI services timeout, rate limit, or return bad output mid-interview
• End-to-end ownership with no separate frontend, backend, or ML teams to hand off to

I am happy to discuss these in depth in an interview context. This post stays focused on outcomes rather than implementation specifics.

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What This Demonstrates for Hiring Teams

If you are evaluating someone for an AI engineering or backend role, this project shows:

• End-to-end AI product development from idea to live deployment
• Agentic AI beyond basic prompt engineering
• Voice AI in a real user-facing product
• Production backend with auth, payments, and persistent data
• Solo shipping ability across frontend, backend, AI, and infrastructure

I built Tyvur because I wanted proof that I could take an AI product from idea to live deployment without a team to hand off to. It is live, handles real users, and required solving problems that only show up when AI meets production.

I am actively looking for my next challenge in the AI and backend space. If your team is building complex LLM applications or scaling AI engineering capabilities, I would love to connect.

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Try It and Connect

• Live product: tyvur.in
• Project details: /projects/tyvur-ai
• Product lessons post: /blog/building-tyvur-first-product-lessons-saas-journey
• GapFoundry case study: /blog/building-gapfoundry-ai-competitive-research-platform-case-study
• Contact: /contact

Would love to hear your thoughts, feedback, or technical questions.